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Furthermore, a decomposition considering missed-speech detection, false alarms, and false speaker labeling is also depicted in Figure1.
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The System 1, for instance, though detecting less correct speakers, maintains a significantly lower number of false speakers.
Here, the Systems 5 and 4 exhibit the highest number of true detected speakers, but at the same time suffer from even higher counts of false speakers.
The possible reason for the high number of false speakers of System 4 could be the substantial initial over-segmentation (reported in Section 'System 4') in a combination with a too strictly defined merging threshold of the dot-scoring similarity.
Firstly, we review the main evaluation metric (DER) and discuss the distributions of its three components (misses, false alarms, speaker errors).
Correctly detected (True) and falsely introduced (False) number of speakers by evaluated systems.
Scores are given for missed speech (MS), false alarms (FA), speaker errors (SPK), and overall diarization error rate (DER).
The central question in the case is whether statements known by the speaker to be false are entitled to First Amendment protection.
DER distribution of missed speech rate (MS), false alarm rate (FA), and speaker error rate (SPKE).
Thus, it is commonly held that there are rules of assertion, and some of these are such that they are violated when the speaker makes a false judgment.
But there are clearly everyday occasions when "delusion" merely refers to a belief that seems obviously false or unwarranted to the speaker.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com